Method and server for traffic signal regulation based on crowdsourcing data

ABSTRACT

Aspects of the disclosure relate generally to a traffic control system, the system includes at least: a Wi-Fi communication unit which connects between vehicles and Internet network; and a control unit which controls the vehicles by a Wi-Fi network. The control unit generates crowdsourcing data by using the data collected from the vehicles, and controls the vehicles based on the generated crowdsourcing data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S.non-provisional utility patent application Ser. No. 14/798,273 filedJul. 13, 2015, which is based upon and claims the benefits ofprovisional patent application No. 62/112,889, entitled “VehicleOperator Prediction and Control Platform,” filed on Feb. 6, 2015. Theentire contents of the above applications are hereby incorporated byreference. International Patent Application No. PCT/US 16/16962, filedFeb. 8, 2016, based upon and claims the benefit of priority to the U.S.non-provisional utility patent application Ser. No. 14/798,273.

DESCRIPTION Background

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Modern vehicles often include navigational hardware or software to aidusers when travelling from one location to another. A user can input adestination and the navigational hardware or software can present one ormore routes from a start location to a destination location. Often routeinformation will include the distance from the start location to thedestination location. Sometimes the route information will include anestimate of the amount of time that it will take to travel from thecurrent location to the destination location based on distance andspeed. The user may select which route to take based on the distance orestimated time. Based on the user selection, the navigational hardwareor software decides a route to the destination.

Meanwhile, various technologies can be utilized for the control ofautonomous vehicles. Some systems involve placing a reference line forthe vehicle to follow, while other systems may have a pre-defined routeprogrammed into a vehicle. In some embodiments, an autonomous vehiclemay be coupled to a track on the ground for guidance purposes. Otherautonomous vehicles may be controlled by a computer and follow a routebased on information stored in the computer.

An autonomous vehicle's navigational hardware or software may allow newroute information to be programmed. A vehicle may be given a new routeto follow based on maps or based on global position system (GPS)signals. Some autonomous vehicles may operate in non-autonomous modewhere they can be driven similar to traditional human-controlledvehicles. When vehicles are driven in an autonomous mode, however, theymay require more precise position information then when driven by ahuman operator.

SUMMARY

In some embodiments, a traffic control system, including at least: aWi-Fi communication unit configured to connect between vehicles andInternet network; and a control unit configured to control the vehiclesby a Wi-Fi network, wherein the control unit is further configured togenerate crowdsourcing data by using the data collected from thevehicles, and control the vehicles based on the generated crowdsourcingdata.

In some embodiments, a method for controlling a traffic, including atleast: connecting, by the Wi-Fi communication unit, via a Wi-Fi network,between vehicles and Internet network; collecting, by the Wi-Ficommunication unit, data from the vehicles; generating, by the controlunit, crowdsourcing data by using the data collected from the vehicles;and controlling, by the control unit, the vehicles based on thegenerated crowdsourcing data.

Details of one or more implementations are set forth in the accompanyingdrawings and the description below. Other features, aspects, andpotential advantages will be apparent from the description and drawings,and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is an environment for controlling a vehicle based oncrowdsourcing data collected by vehicles, in accordance with one or moreembodiments.

FIG. 2 is a flow diagram of an embodiment of a process for controlling avehicle based on crowdsourcing data from vehicles, in accordance withone or more embodiments.

FIG. 3 is a flow diagram of another embodiment of a process forcontrolling a vehicle based on crowdsourcing data from vehicles, inaccordance with one or more embodiments.

FIG. 4 is a flow diagram of another embodiment of a process forcontrolling a vehicle based on crowdsourcing data from vehicles, inaccordance with one or more embodiments.

FIG. 5 is a schematic illustrating a conceptual partial view ofoverlapping crowdsourcing data, in accordance with one or moreembodiments.

FIG. 6 is a block diagram illustrating an embodiment of a vehicle, inaccordance with one or more embodiments.

FIG. 7 is a block diagram illustrating an embodiment of an electroniccontrol unit (ECU) embedded in a vehicle, in accordance with one or moreembodiments.

FIG. 8 is a block diagram illustrating an embodiment of a server, inaccordance with one or more embodiments.

FIG. 9 is a block diagram illustrating a system for controlling avehicle based on crowdsourcing data collected by vehicles, in accordancewith one or more embodiments.

FIG. 10 is a flow diagram of an embodiment of a process for controllinga traffic light based on crowdsourcing data, in accordance with one ormore embodiments.

FIG. 11 is an environment for controlling a traffic light based oncrowdsourcing data, in accordance with one or more embodiments.

FIG. 12 is an environment for controlling vehicles by using a trafficcontrol system, in accordance with one or more embodiments.

FIG. 13 is a block diagram illustrating a traffic control system forcontrolling vehicles, in accordance with one or more embodiments.

FIG. 14 is a flow diagram of a method for controlling a traffic, inaccordance with one or more embodiments.

FIG. 15 is a flow diagram of a method for controlling a traffic, inaccordance with one or more embodiments.

FIG. 16 is a schematic illustrating a conceptual partial view of afragmented signal for controlling signs or colors emitted from a trafficlight unit, in accordance with one or more embodiments.

All arranged in accordance with at least some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the disclosed subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are examples and are notintended to be limiting.

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments are also utilized, and other changes are also made, withoutdeparting from the spirit or scope of the subject matter presentedherein. It will be readily understood that the aspects of the presentdisclosure, as generally described herein, and illustrated in thefigures, can be arranged, substituted, combined, separated, and designedin a wide variety of different configurations, all of which areexplicitly contemplated herein.

The methods, vehicle, and server disclosed herein generally relate tocontrol of a vehicle based on crowdsourcing data from other vehicles. Insome embodiments, the methods, vehicle, and server disclosed hereinrelate to control of a vehicle in autonomous mode.

FIG. 1 is an environment for controlling a vehicle based oncrowdsourcing data collected by vehicles, in accordance with one or moreembodiments.

In an example embodiment, the crowdsourcing-based autonomous vehiclecontrolling environment 100 includes a plurality of vehicles 101, 102and 103, a wireless node 201 or 202, and a server 300. The wireless node201 or 202 includes a transmitter and receiver. The vehicles 101, 102and 103 are configured to perform autonomous driving based on navigationinformation received from server 300, and the vehicles 101, 102 and 103are equipped with a sensor which collects driving data, and equippedwith a communication unit (not shown) configured to communicate with thewireless node 201 or 202, via wireless network. The wireless node 201 or202 is configured to transmit data between the vehicles 101, 102 and 103and the server 300. The server 300 is configured to analyze datatransmitted from the vehicles 101, 102 and 103, via the wireless node201 or 202 or directly from the vehicles 101, 102 and 103.

In some embodiments, each of the vehicles 101, 102 and 103 is equippedwith a sensor configured to detect driving data. The driving data isbased on the driver's actions or commands. For example, the driver'sactions include at least one of timing of accelerating, braking orsteering wheel rotating, and amount of accelerating, braking or steeringwheel rotating. Navigation information is stored in and retrieved from avehicle memory, in a database, or on the Internet. In some embodiments,new navigation information is received by server 300 wirelessly over anetwork such as a cellular network, an 802.11 network, or a WirelessWide Area Network (WWAN), via the wireless node 201, 202 or directlyfrom the vehicles. The vehicles transmit driving data to the server 300,via the wireless node 201 or 202, or directly. The driving data includesat least one of breaking data, acceleration data and steering data. Insome embodiments, each of the vehicles 101, 102 and 103 communicateswith the server 300, in accordance with a predetermined cycle of timeor, when the vehicles detect an event during the driving to thedestination. In some embodiments, each of the vehicles 101, 102 and 103includes a computer system to provide data processing.

In some embodiments, the autonomous driving of the vehicles 101, 102 and103 are performed by a Motor Driven Power Steering (MDPS) system. TheMotor Driven Power Steering (MDPS) system is a motor driven steeringsystem assisting steering power by using power from a motor withoutusing hydraulic pressure. The MDPS system is disposed in vehicles. TheMDPS is equipped with a decelerator. The decelerator includes aworm-shaft/worm wheel rotated by a motor to assist a steering force. Thedecelerator further includes a motor driven column apparatus having atilt motor. The decelerator further a telescopic motor to implement tiltand telescopic movement. The MDPS motor is controlled by an MDPSElectronic Control Unit (ECU).

In some embodiments, a driver of the vehicle accesses the server that isconfigured to control the vehicle based on crowdsourcing data through asoftware application. In some embodiments, the software application is awebsite or a web application compatible with a hardware system discussedsubsequently. In some embodiments, the software application is, in-part,configured by a hardware system that is embedded in the vehicle. Thoughthe present disclosure is written with reference to a device embedded ina vehicle, it should be understood that any computing device, in someembodiments, is employed to provide the various embodiments disclosedherein.

In some embodiments, the vehicles measure the driver's actions made bydrivers of the vehicles, and store such actions. Such actions areinterpreted to predict vehicle operator behavior in one or more drivingsituations. The aforementioned predictions are used to createsemi-autonomous or autonomous control of the vehicles. In addition, theactions of drivers of the multiple vehicles are combined to provide awider range of application.

In some embodiments, route data is collected from other vehicles andassists a vehicle to drive autonomously. A hardware system (e.g.,computing device 1100 shown in FIG. 7) is configured to store steering,acceleration, braking data in a memory storage of the hardware system,and then, the stored data is employed to drive vehicle autonomously.Moreover, embodiments comprise embedded software logic configured tooverride an autonomous driving system. Overrides are triggered if a dataflag is raised by a connected sensor, for safety and instantlyre-synchronizes route data to the driving system.

The detailed configuration of the vehicles 101, 102, and 103 accordingto some embodiments will be described below with reference to belowfigures. In some embodiments, the wireless node 201 or 202 is at leastone of a satellite, terrestrial repeater, Global Positioning System(GPS) signal repeater, and cellular signal repeater. The wireless nodecommunicates with the server 300 or the vehicles, in accordance with apredetermined cycle or, when the wireless node receives a predeterminedevent signal from at least one of the vehicles. For example, thepredetermined event signal includes a signal of departing of a vehicleand a signal of arriving of a vehicle.

In some embodiments, the server 300 is a cloud computing service. Theserver 300 performs aspects of computing such as, in some embodiments,data analysis. In some embodiments, the server 300 is centralized. Insome embodiments, vehicles transmit the driving data to server 300(e.g., navigation server) over network 400 (e.g., the internet), via awireless node. In some embodiments, the vehicles 101, 102, and 103connect to the network 400 through cellular (or Wi-Fi) wireless node (orwireless access point) 201 or 202. In some implementations, the drivingdata collected by vehicles 101, 102 and 103 is used to generatecrowdsourcing data to be used for controlling the vehicles. In someembodiments, the server 300 collects driving data from a plurality ofvehicles, correlates the traffic information, route conditioninformation. The driving data is collected and organized based onlocation and time. The server 300 also generates the crowdsourcing databased on the collected driving data. FIG. 2 is a flow diagram of anembodiment of a method for controlling a vehicle based on crowdsourcingdata from vehicles, in accordance with one or more embodiments.

At operation S201, a vehicle (e.g., vehicle 101, 102, or 103 shown inFIG. 1) transmits a request for a route to a destination, to a server(e.g., server 300 shown in FIG. 3). The server receives the request fromthe vehicle. In some embodiments, the request is input by a driver ofthe vehicle. The vehicle receives an input from the driver for thedestination. The vehicle is configured to provide an interface for adriver by way of a data input device. In some embodiments, the interfaceis a touch screen. In some embodiments, the request is automaticallygenerated by the vehicle. The vehicle determines the destination basedon a travel log stored in the vehicle. Meanwhile, the vehicle isconnected with the server via at least one of a cellular network, aWi-Fi network and a satellite network. The request is transmitted via atleast one of the cellular network, the Wi-Fi network and the satellitenetwork. In some embodiments, the request is transmitted via a wirelessnode, or directly transmitted, to the server.

At operation S202, the server retrieves navigation information thatincludes the requested route. The navigation information is autonomousnavigation information which includes vehicle controlling information.In some embodiments, the navigation information is stored in the server,or generated when the route request is transmitted from the vehicle. Insome embodiments, the navigation information includes controllinginformation for steering wheel rotation, accelerating, and breaking.

At operation S203, the server transmits the retrieved navigationinformation, to the vehicle. The vehicle receives the retrievednavigation information from the server. In some embodiments, theretrieved navigation information is transmitted via at least one of thecellular network, the Wi-Fi network and the satellite network. In someembodiments, the retrieved navigation information is transmitted via awireless node, or directly transmitted, to the vehicle.

At operation S204, the operation of the vehicle is configured to becontrolled based on the transmitted navigation information. In someembodiments, based on the transmitted navigation system, a motor drivenpower steering (MDPS) in which an electric motor which does not usehydraulic pressure is used or electro-hydraulic power steering (EHPS) inwhich an electric pump which is actuated by a motor rather than by thedriving power of the engine is used for controlling the vehicle. In someembodiments, the MDPS and the EHPS are combined. In some embodiments,the MDPS and the EHPS perform complementary functions and auxiliarysteering power is provided at an emergency steering failure in order toguarantee stability. In this example, the vehicle includes the MDPSwhich assists steering power using the torque of a main motor and theEHPS which assists the steering power using hydraulic pressure that isgenerated by actuation of a hydraulic pump. The MDPS is used as a mainsteering device, and the EHPS is used as an auxiliary steering device inorder to assist insufficient steering power during emergencymalfunctioning of the motor or in heavy duty vehicles. For autonomouscontrolling motors of the MDPS and the EHPS are controlled by anElectronic Control Unit (ECU). In this example, controlling is performedbased on controlling information included in the navigation information.An acceleration, and braking of the vehicle are also controlled by theECU, with reference to the controlling information included in thenavigation information. The detailed configuration of the vehicle to becontrolled by the navigation information, according to some embodiments,is described in FIG. 7.

At operation S205, while driving, the vehicle collects data regardingthe vehicle driving operation, according to the transmitted navigationinformation. The data regarding vehicle driving operation includesdriving data. In some embodiments, the data of driver's action includesinformation on at least one of a steering rotation, accelerating andbreaking. In some embodiments, the vehicle collects the data, duringdriving to the destination, in accordance with a predetermined cycle. Inother embodiments, the vehicle collects the data, during driving to thedestination when the vehicle detects an event during the driving to thedestination. The event is detected based on data defining an event to bedetected and based on sensing data.

In some embodiments, the vehicle generates sensing data by using aplurality of sensors equipped in the vehicle. In some embodiments, thesensors include at least some of a distance sensor, infra-red sensor, apressure sensor, a speed sensor, a motion sensor, a light sensor, aproximity sensor, a global navigation satellite system (GNSS) (e.g., GPSreceiver), a temperature sensor, a biometric sensor, or other sensingdevice, to facilitate related functionalities. The vehicle collects thegenerated sensing data, and generates the collected data based on thecollected sensing data.

In some embodiments, the plurality of sensors transmits the sensing datato the driving control unit via a wireless network or wired network. Acontroller area network (CAN), a local area network (LAN), or a serialnetwork is used for the transmission of the sensing data.

In some embodiments, at least one of the plurality of sensors detects atraffic light during driving of the vehicle, and a stop of the vehicleat a red right of the detected traffic light. The vehicle uses thedetected traffic light information for controlling a speed of thevehicle. In some embodiments, the server transmits stop sign/stop lightattribute information to the vehicle. In some embodiments, the vehiclereceives stop sign/stop light attribute information for a geographicarea as a navigation information from the server and the servertransmits the stop sign/stop light attribute information to the vehiclefor autonomous controlling.

At operation S206, the vehicle transmits the collected data to theserver. The server receives the collected data from the vehicle. In someembodiments, the collected data is transmitted via at least one of thecellular network, the Wi-Fi network and the satellite network. In someembodiments, the collected data is transmitted via a wireless node, ordirectly transmitted, to the server. In some embodiments, the vehicletransmits the collected data in accordance with a predetermined cycle.In other embodiments, the vehicle transmits the collected data when thevehicle detects an event during the driving to the destination. Theevent is detected based on data defining an event to be detected andbased on sensing data.

At operation S207, the server generates crowdsourcing data based on thecollected data transmitted from a plurality of vehicles. Beforegenerating the crowdsourcing data, the server receives the collecteddata from the vehicles. The server generates crowdsourcing data by usingthe received data. In some embodiments, the crowdsourcing data ingenerated by overlapping the collected data for overlapped route in thedata from the vehicles. The detailed description of the overlappedinformation, according to some embodiments, will be described below withreference to FIG. 3.

At operation S208, the server updates stored navigation informationbased on the crowdsourcing data, and transmits the updated navigationinformation to the vehicle. In some embodiments, the server updates andtransmits the navigation information when the crowdsourcing data isaccumulated more than a threshold amount. At operation S209, the vehiclereceives the crowdsourcing data from the server. In some embodiments,the data is transmitted via at least one of the cellular network, theWi-Fi network and the satellite network. In some embodiments, the datais transmitted via a wireless node, or directly transmitted, to thevehicle.

In some embodiments, the server transmits the updated navigationinformation to the vehicle, and the vehicle receives the updatednavigation information from the server. In some embodiments, the serverprocesses the updated navigation information (e.g., filtering, noisecancelling, smoothing, or vectoring), and then transmits the processednavigation information to the vehicle.

At operation S210, the vehicle updates the navigation information basedon the received external data. The vehicle combines the receivedcrowdsourcing data and the collected data, and updates the navigationinformation based on the combined data. In some embodiments, the updateis performed in accordance with a predetermined cycle.

At operation S211, the vehicle controls the operation of the vehiclebased on the updated navigation information. During the driving based onthe navigation information, when the vehicle detects a preset unusualevent, the vehicle changes a driving mode of the vehicle to adriver-control mode. The preset unusual event, for example, includes anevent that is not registered in the vehicle. The preset unusual event isdetected based on data defining an event to be detected and based onsensing data. The controlling of the vehicle includes at least one of aspeed controlling of the vehicle and a steering wheel angle controllingof the vehicle.

FIG. 3 is a flow diagram of another embodiment of a method 300 foroverriding a vehicle based on crowdsourcing data from vehicles, inaccordance with one or more embodiments.

Method shown in FIG. 3 is performed during autonomous vehicle-driving.In some embodiments, in operation S204 or in operation S211 of FIG. 2,the ECU of the vehicle (e.g., vehicle 101, 102, or 103 shown in FIG. 1)is configured to override the autonomous vehicle-driving based onsensing data in accordance with the method shown in FIG. 3.

In operation S301, autonomous driving is performed. In the autonomousdriving, a vehicle (e.g., vehicle 101, 102, or 103 shown in FIG. 1) iscontrolled based on crowdsourcing data, sensing data and/or navigationinformation, as described in FIG. 2. In some embodiments, in theautonomous driving, the vehicle's steering wheel, accelerator, and breakare autonomously controlled.

At operation S302, the vehicle senses a surrounding area of the vehicle,during driving of the vehicle. In some embodiments, the vehiclegenerates sensing data regarding the surrounding area of the vehicle, byusing a plurality of sensors equipped in the vehicle. The sensorsinclude at least some of a distance sensor, infra-red sensor, pressuresensor, and speed sensor.

At operation S303, the vehicle detects an event based on sensing data.The event includes at least one of detecting obstacle in a route,impacting from the outside vehicle, impacting from the inside vehicle,vibrating of the vehicle, and waving of the vehicle. The event isdetected based on a matching result between event data and sensing data.The data defining the event is stored in the vehicle or the sensor. Insome embodiments, the vehicle detects the event when an amount of thesensing data of the event is greater than the predetermined thresholdamount.

At operation S304, the vehicle checks whether the event is an event tooverride the autonomous driving of the vehicle. In some embodiments,information regarding the event to override the autonomous driving ofthe vehicle is stored in the vehicle, and the vehicle performs thechecking by comparing the stored information with the sensing data. Insome embodiments, the vehicle determines whether to override theautonomous driving when an amount of the sensing data of the event isgreater than the predetermined threshold amount. In some embodiments,the vehicle determines whether to override the autonomous driving when aduration of sensing of the event is longer than the predeterminedthreshold duration. If the vehicle determines to override the autonomousdriving of the vehicle, the operation proceeds to operation S305. If thevehicle determines to not override the autonomous driving of thevehicle, the operation proceeds to operation S301.

If the vehicle determines to override the autonomous driving of thevehicle, at operation S305, the vehicle halts the autonomous driving. Insome embodiments, the vehicle outputs a notification to a driver of thevehicle before halting the autonomous driving. In some embodiments,after the halting operation, the vehicle performs a predeterminedevasive action based on the navigation information. In some embodiments,the predetermined evasive action includes at least one of breaking,accelerating, and turning.

After the performing the predetermined evasive action, the vehicle, atoperation S306, returns to the autonomous driving. In some embodiments,the autonomous driving is re-synchronized with reference to locationdata of the vehicle and navigation information. Meanwhile, when thevehicle determines that the detected event in the operation S304 is notan event configured to override the autonomous driving of the vehicle,the vehicle keeps performing the autonomous driving S301.

At operation S307, the vehicle detects whether the vehicle arrives at adestination. The vehicle uses the navigation information and the currentlocation of the vehicle for operation S307. If the vehicle determinesarrival, the autonomous driving is ended. If the vehicle determines thatarrival is not detected, the vehicle keeps performing the autonomousdriving S301. In some embodiments, if the vehicle detects that adistance between the current location of the vehicle and the destinationis less than a threshold distance, the vehicle outputs a notification.The notification includes at least one of message displaying, alarmsounding, vibrating, or alike which notify some information to a driverof the vehicle.

FIG. 4 is a flow diagram of another embodiment of a method 400 forcontrolling a vehicle based on crowdsourcing data from vehicles, inaccordance with one or more embodiments.

In some embodiments, at the vehicle, before performing the autonomousvehicle-driving, in some embodiments, between operation S201 andoperation S204 of FIG. 2, the ECU of the vehicle performs pre-operationfor the autonomous driving. The operations of the pre-operation aredescribed as below.

At operation S401, a vehicle receives a destination from a driver of thevehicle. For another example, the request is automatically generated bythe vehicle. The vehicle determines the destination based on a travellog stored in the vehicle.

At operation S402, the vehicle detect a current location. In someimplementations, the vehicle includes sensors, software and/or hardwarefor determining or tracking the location of the vehicle. In someembodiments, the vehicle includes a GNSS receiver for determining thelocation of the vehicle. The vehicle receives satellite signals from oneor more GNSS satellites 106 and determine the location of the vehiclebased on the satellite signals according to known methods. The vehicleinclude cellular and/or Wi-Fi transceivers for receiving andtransmitting cellular and/or Wi-Fi signals. The cellular and/or Wi-Fisignals can be used to determine a location for the vehicle based onknown locations for the cellular or Wi-Fi wireless nodes that aretransmitting the cellular or Wi-Fi signals. In some embodiments, thevehicle uses triangulation or location averaging to determine thelocation of the vehicle based on the cellular and/or Wi-Fi signals.

At operation S403, the vehicle receives the navigation information fromthe server. The navigation information includes data for autonomouscontrolling the vehicle. In some embodiments, the data is crowdsourcingdata for controlling the vehicle. In some embodiments, the navigationinformation is transmitted via at least one of the cellular network, theWi-Fi network and the satellite network. In some embodiments, thenavigation information is transmitted via a wireless node, or directlytransmitted, to the vehicle.

At operation S404, the vehicle, when an amount of the crowdsourcing datarelated to the detected current location is less than a thresholdamount, the ECU navigates or guides the vehicle to a different locationassociated with an amount of crowdsourcing data that is greater than thethreshold amount. The different location is determined as a startingpoint. In some embodiments, the different location is the closestlocation from the current location of the vehicle, among the locationswhich have over-threshold amount crowdsourcing data.

In some embodiments, the server transmits information to the vehicle,such as, in some embodiments, the closest location for which thedatabase has navigation information or crowdsourcing data. In thisembodiment, the vehicle then displays to the driver the closest pointand the driver then manually operates the vehicle in order to travel tothe displayed location. In this embodiment, once the server or vehicledetects that the vehicle is in a location for which it has adequate dataor adequate amount data, it performs some autonomous control of thevehicle.

At operation S405, the vehicle is controlled based on the navigationinformation, as described in the operation S204 of FIG. 2. In someembodiments, the vehicle's steering wheel, accelerator, and break areautonomously controlled for autonomous driving of the vehicle.

At operation S406, the vehicle determines if a change of route occurred.In some embodiments, the vehicle detects the change of the route whenthe vehicle drives on a route other than the route determined by thenavigation information.

At operation S407, when the vehicle detects the route change, the ECUnavigates or guides the vehicle to a next location which is the closestlocation and is on the determined route. And then, at operation S407,the vehicle keeps performing autonomous driving. In some embodiments,the autonomous driving is re-synchronized with reference to locationdata of the vehicle and navigation information.

Meanwhile, if the vehicle does not detect the change of route, the ECU,at S405, keeps performing autonomous driving.

In some embodiments, when a distance between the detected currentlocation and a location of which an amount of the crowdsourcing data isless than a threshold amount, is less than a threshold distance, thevehicle outputs a notification. The notification includes at least oneof sound notification, vibration notification, text notification, andimage notification.

FIG. 5 is a schematic illustrating a conceptual partial view ofoverlapping crowdsourcing data, in accordance with one or moreembodiments.

In some embodiments, a vehicle or a server generates navigationinformation by overlapping a plurality of crowdsourcing data. In someembodiments, the server receives first crowdsourcing data from a firstvehicle, and receives second crowdsourcing data from a second vehicle.The server generates the navigation information by overlapping the firstcrowdsourcing data and the second crowdsourcing data. The server sendsthe generated navigation information to vehicles for autonomouscontrolling. In some embodiments, the vehicle performs correspondingoperations to the above of the server, to generate the navigationinformation.

In some embodiments, the server determines an overlapped section whichis a part of the route to the destination and is included in each of thedriving sections of the first crowdsourcing data and the driving sectionof the second crowdsourcing data. In this embodiment, the server thenextracts third crowdsourcing data regarding the overlapped section, fromthe first crowdsourcing data, and extracts fourth crowdsourcing dataregarding the overlapped section, from the second crowdsourcing data.The server generates the navigation information by combining the thirdand fourth crowdsourcing data.

The exemplary overlapping is described in FIGS. 5(a) and 5(b). As shownin FIG. 5(a), a first driver drives a route D1, between 501 and 503, asecond driver drives a route D2, between 502 and 504, and a third driverdrives a route D3, between 501 and 504. The route D3 is covered by theroute D1 and the route D2, and the route between 502 and 503 isoverlapped.

As shown in FIG. 5(b), a first vehicle of the first driver collectsdriving data 505 for the route D1, and a second vehicle of the seconddriver collects driving data 506 for the route D2. Data (e.g. speeds,distances, vehicle operator actions, etc.) is collected by sensorsembedded in the vehicles. Based on the driving data for the route D1 andthe driving data for the route D2, navigation information 507 for routeD3 is generated.

FIG. 6 is a block diagram illustrating an embodiment of a vehicle, inaccordance with one or more embodiments.

In some embodiments, a hardware system includes one or more sensors 602and 603. The sensors 602 and 603 include sensors for receiving operatorinput. In some embodiments, sensors include one or more sensors formeasuring steering torque and rotation, and gas pedal and brake pedaldepression. In some embodiments, the vehicle further includes sensors602 for detecting surrounding circumstances. Sensors 602 include camerasfor detecting position relative to the road, traffic signs and signals,and obstacles in the road as well as velocity, acceleration and distancemeasuring devices. In some embodiments, sensors 603 include detectingsurrounding sensors include, location determining devices, globalpositioning systems and triangulation systems.

At least one of sensors 602 and 603 interfaces with an electroniccontrol unit (hereafter, ECU; 604). The ECU includes a computing device.Further, the ECU 604 includes volatile and non-volatile memory devices,such as, in some embodiments, RAM, ROM, flash memory, one or more harddrives and removable memory, and one or more devices for wirelesslytransmitting data. The ECU 604 controls the motor 606 of the vehicle.The exemplary configuration of the ECU is described in FIG. 7.

In some embodiments, hardware 605 utilizes software for storing andtransmitting the data. In some embodiments, the software incorporatesmethods for embedding metadata and transferring it wirelessly to adatabase.

The ECU 604 interfaces with the controls of the vehicle. In someembodiments, controls include motors, servos, pistons and computercontrols that operate the speed and direction of the vehicle. Furthercontrols operate auxiliary mechanisms, such as, in some embodiments,blinkers or horns to alert other vehicle operators.

In some embodiments, the vehicle further provide an interface for adriver of the vehicle by way of a data input device, such as, a touchscreen. The driver, in some embodiments, select a destination on a datainput device. The interface also displays relevant information to thevehicle operator, such as, in some embodiments, a map.

Data is acquired by sensors 602 and 603, during some or all of thevehicle operation. Data is stored in local memory or external memoryembedded in hardware 605. Some or all of the acquired data is uploadedto the database in hardware 605. The database analyzes the data or embedthe data as metadata. In some embodiments, the database is embedded inthe server (e.g., server 300 shown in FIG. 1) which provides navigationinformation for the vehicle.

In some embodiments, recirculating ball 607 transmits power generated bythe motor 606 to a worm gear of the vehicle for rotating the worm gear.The recirculating ball is a steering mechanism in older automobiles,off-road vehicles, and some trucks. By using the recirculating ball,vehicles use rack and pinion steering. The recirculating ball steeringmechanism contains a worm gear inside a block with a threaded hole init; this block has gear teeth cut into the outside to engage the sectorshaft (also called a sector gear) which moves the Pitman arm. Thesteering wheel connects to a shaft, which rotates the worm gear insideof the block. Instead of twisting further into the block, the worm gearis fixed so that when it spins, it moves the block, which transmits themotion through the gear to the pitman arm, causing the road wheels toturn.

FIG. 7 is a block diagram illustrating an embodiment of an electroniccontrol unit (ECU) embedded in a vehicle, in accordance with one or moreembodiments.

Consistent with embodiments of the disclosure, the aforementioned memorystorage and processing unit are implemented in a computing device, suchas computing device 700 of FIG. 7. In some embodiments, the computingdevice 700 is an ECU or a part of the ECU. Any suitable combination ofhardware, software, or firmware is used to implement the memory storageand processing unit. In some embodiments, the memory storage andprocessing unit is implemented with computing device 700 or any of othercomputing devices 718, in combination with computing device 700. Theaforementioned system, device, and processors are examples and othersystems, devices, and processors comprise the aforementioned memorystorage and processing unit, consistent with embodiments of thedisclosure.

In some embodiments, the computing device 700 controls an operation ofthe vehicle based on the received navigation information. The computingdevice collects the sensing data during driving according to thereceived navigation information, and generates driving data based on thecollected sensing data. The computing device transmits the generateddriving data to the server. The computing device receives crowdsourcingdata from the server, and then, updates the received navigationinformation based on the received crowdsourcing data. The computingdevice controls the operation of the vehicle, based on the updatednavigation information.

With reference to FIG. 7, an ECU consistent with embodiments of thedisclosure includes a computing device, such as computing device 700. Ina basic configuration, computing device 700 includes at least oneprocessing unit 702 and a system memory 704. System memory 704 comprisevolatile (e.g. random access memory (RAM)), non-volatile (e.g. read-onlymemory (ROM)), flash memory, or any combination. System memory 704includes operating system 705, one or more programming modules 706, andincludes a program data 707. Operating system 705, in some embodiments,is suitable for controlling computing device 700's operation. In someembodiments, programming modules 706 includes application 720.Furthermore, embodiments of the disclosure is practiced in conjunctionwith a graphics library, other operating systems, or any otherapplication program and is not limited to any particular application orsystem. This configuration is shown in FIG. 7.

In some embodiments, computing device 700 also includes additional datastorage devices (removable and/or non-removable) such as, in someembodiments, magnetic disks, optical disks, or tape. Such additionalstorage is illustrated in FIG. 7 by a removable storage 709 and anon-removable storage 710. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Systemmemory 704, removable storage 709, and non-removable storage 710 are allcomputer storage media examples (i.e., memory storage.) Computer storagemedia includes, but is not limited to, RAM, ROM, electrically erasableread-only memory (EEPROM), flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which is used to store informationand which is accessed by computing device 700. Computing device 700 hasinput device(s) 712 such as a keyboard, a mouse, a pen, a sound inputdevice, a touch input device, etc. Output device(s) 714 such as adisplay, speakers, a printer, etc. is also included. The aforementioneddevices are examples.

Computing device 700 also contains a communication connection 716 thatallows computing device 700 to communicate with other computing devices718, such as over a network in a distributed computing environment, insome embodiments, an intranet or the Internet. Communication connection716 is one example of communication media. Communication media isembodied by computer readable instructions, data structures, programmodules, or other data in a modulated data signal, such as a carrierwave or other transport mechanism, and includes any information deliverymedia. The term “modulated data signal” describes a signal that has oneor more characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency (RF), infrared, and other wireless media. The term computerreadable media as used herein includes both storage media andcommunication media.

A number of program modules and data files is stored in system memory704, including operating system 705. While executing on processing unit702, programming modules 706 (e.g., application 720) performs processesincluding, in some embodiments, one or more of the methods as describedabove and illustrated in the figures. The aforementioned process is anexample. Other programming modules that are used in accordance withembodiments of the present disclosure includes electronic mail andcontacts applications, word processing applications, spreadsheetapplications, database applications, slide presentation applications,drawing or computer-aided application programs, etc.

FIG. 8 is a block diagram illustrating an embodiment of a server, inaccordance with one or more embodiments.

As shown in FIG. 8, the server 800 includes a communication unit 801,storage 802, a control unit 803, and an analysis unit 804. In someembodiments, the server 300 shown in FIG. 1 has the same configurationwith the server 800 shown in FIG. 8.

The communication unit 801 receives a request for a route to adestination, from a vehicle. Further, the communication unit 801transmits crowdsourcing data or navigation information to the vehicle.

The storage 802 stores route/navigation information.

The analysis unit 804 generates navigation information based on thestored route information in the storage 802. In some embodiments, theroute information is map data. Further, the route information includesdriving data collected by a plurality of vehicles.

In some embodiments, the control unit 803 retrieves navigationinformation, according to the received request from the vehicle. Thecontrol unit 803 transmits the retrieved navigation information to thevehicle, and receives collected data from a plurality of vehicles. Then,the control unit 803 combines the received collected data. The controlunit 803 extracts a route-based data corresponding to the route of thevehicle, and generates the crowdsourcing data by using the extractroute-based data. The control unit 803 transmits the generatedcrowdsourcing data to one or more vehicles.

In some implementations, a vehicle (101, 102 or 103 of FIG. 1) transmitsor reports driving data to the server 800 (e.g., navigation server) overnetwork (e.g., the internet). In some embodiments, vehicles connect tothe network through cellular (or Wi-Fi) wireless node or wireless accesspoint (201 or 202 of FIG. 2). In some implementations, the driving datacollected from the vehicles is used to determine navigation informationassociated with steering wheel handling, accelerating and breaking.

In some implementations, the driving data is reported to the server 800in real-time or near real-time. In some implementations, the drivingdata is stored on vehicles and reported to the server 800 at a latertime. In some embodiments, vehicles are not connected to the networkwhen the traffic information is collected. Thus, the vehicles stores thedriving data to an internal storage device and report the driving datato the server 800 when the vehicles later establish a connection withthe network.

In some embodiments, the control unit 803 generates the crowdsourcingdata by using collected traffic data, and controls a traffic light basedon the generated crowdsourcing data. The traffic light includes a set ofelectrically operated signal lights (e.g., a red light, a yellow light,and a green light) used to direct or control traffic at a road orintersections. In some embodiments, the red light indicates that trafficmust stop and the green light that it may go, and usually an amberwarning light or a yellow light is added between the red and the green.In some embodiments, the traffic data includes one or more data amongtraffic volume data, driver age data, and driver reaction speed data.The traffic data is able to be transmitted to the server 800 from one ormore vehicles in a compressed state and/or in an encrypted state.

In some embodiments, the control unit 803 analyzes a traffic volumebased on the crowdsourcing data, controls the traffic light to turn on agreen light for a longer time than a predetermined time when theanalyzed traffic volume is higher than a predetermined level, controlsthe traffic light to turn on the green light for the predetermined timewhen the analyzed traffic volume is equal to the predetermined level,and controls the traffic light to turn on the green light for a shortertime than the predetermined time when the analyzed traffic volume islesser than the predetermined level. In some embodiments, the trafficvolume indicates a number of vehicles crossing a section of road perunit time at a selected period and/or an average speed of vehiclescrossing the section of the road per unit time at the selected period.In some embodiments, the predetermined level is set by a manufacture ofthe server or by a user of the server, or is updated by the user of theserver. In some embodiments, the predetermined time is set by themanufacture of the server or by the user of the server, or is updated bythe user of the server.

In some embodiments, the control unit 803 analyzes a traffic volumebased on the crowdsourcing data, controls the traffic light to turn on ared light for a shorter time than a predetermined time when the analyzedtraffic volume is higher than a predetermined level, controls thetraffic light to turn on the red light for the predetermined time whenthe analyzed traffic volume is equal to the predetermined level, andcontrols the traffic light to turn on the red light for a longer timethan the predetermined time when the analyzed traffic volume is lesserthan the predetermined level.

In some embodiments, the control unit 803 analyzes an average age ofdrivers of the plurality of vehicles based on the crowdsourcing data,controls the traffic light to change colors of light (e.g., from a greenlight to a yellow light, from the yellow light to a red light, and fromthe red light to the green light) in a higher speed than a predeterminedspeed when the analyzed average age is lesser than a predeterminedlevel, controls the traffic light to change the colors of light in thepredetermined speed when the analyzed average age is equal to thepredetermined level, and controls the traffic light to change the colorsof light in a lower speed than the predetermined time when the analyzedaverage age is higher than the predetermined level.

In some embodiments, the control unit 803 analyzes a reaction speed ofdrivers of the plurality of vehicles based on the crowdsourcing data,controls the traffic light to change colors of light in a higher speedthan a predetermined speed when the analyzed reaction speed is higherthan a predetermined level, controls the traffic light to change thecolors of light in the predetermined speed when the analyzed reactionspeed is equal to the predetermined level, and controls the trafficlight to change the colors of light in a lower speed than thepredetermined time when the analyzed reaction speed is slower than thepredetermined level. In some embodiments, the reaction speed is inputtedby a driver of a vehicle on a road. In some embodiments, the reactionspeed is automatically determined based on driving data of the vehicleon the road. In some embodiments, the driving data includes one or moreof a reaction timing to a red light of a traffic light, a reactiontiming to an interfere of another vehicle, and a reaction trimming to astop sign on the road.

In some embodiments, the control unit 803 collects traffic sensing dataon a road by using sensor 805 included in the server and configured tosense the plurality of vehicles and objects surrounding the vehicles,and generates the crowdsourcing data by using the collected traffic dataand the collected traffic sensing data. In some embodiments, the sensor805 uses Lidar (also written LIDAR, LiDAR or LADAR), which is a remotesensing technology that measures distance by illuminating a target witha laser and analyzing the reflected light. In some embodiments, thecontrol unit 803 controls the traffic light based on the traffic sensingdata without using the traffic data which is collected by andtransferred from a plurality of vehicles. In these embodiments, thecontrol unit is able to improve an accuracy of the control of thetraffic light because the traffic data collected by the vehiclesincludes redundancy data.

In some embodiments, one or more of the elements 801-805 is/areimplemented by, or include(s), one or more processors and/orapplication-specific integrated circuits (ASICs) specified forrespectively corresponding operations and functions described herein inthe present disclosure. In some embodiments, the methods according to atleast one embodiment of the present disclosure are implemented ascomputer-readable code on a non-transitory computer-readable recordingmedium. The non-transitory computer-readable recording medium includesany data storage device configured to store data readable and/orexecutable by a computer system. Examples of the non-transitorycomputer-readable recording medium include, but are not limited to,magnetic storage media (e.g., magnetic tapes, floppy disks, hard disks,etc.), optical recording media (e.g., a compact disk read only memory(CD-ROM) and a digital video disk (DVD)), magneto-optical media (e.g., afloptical disk), and hardware devices that are specially configured tostore and execute program instructions, such as a ROM, a random accessmemory (RAM), a flash memory, etc. In some embodiments, data, such asvarious sequences or personal markers described herein, are stored on anon-transitory computer-readable recording medium.

FIG. 9 is a block diagram illustrating a system for controlling avehicle based on crowdsourcing data collected by vehicles, in accordancewith one or more embodiments.

In 1^(st) layer, Self Driving Car corresponds to the vehicle 101, 102 or103 of FIG. 1. Sensors correspond to the sensors 602 and 603 in FIG. 6,Analytics corresponds data stored in Hardware 605 in FIG. 6, and VehicleChassis correspond the MDPS which is aforementioned. In 2^(nd) layer,Central Agent corresponds to control unit 803 in FIG. 8, DataCollector/Transmitter corresponds to analysis unit 804 in FIG. 8, andData Streaming corresponds to communication unit 801 in FIG. 8. InVehicle Cloud, sensors correspond to the sensors 602 and 603 in FIG. 6,and Data Collector/Transmitter corresponds to Hardware 605 in FIG. 6.The above disclosed correspondences are non-limiting embodiments.

In some embodiments, one or more of the parts shown in FIG. 9 is/areimplemented by, or include(s), one or more processors and/orapplication-specific integrated circuits (ASICs) specified forrespectively corresponding operations and functions described herein inthe present disclosure. In some embodiments, the methods according to atleast one embodiment of the present disclosure are implemented ascomputer-readable code on a non-transitory computer-readable recordingmedium. The non-transitory computer-readable recording medium includesany data storage device configured to store data readable and/orexecutable by a computer system. Examples of the non-transitorycomputer-readable recording medium include, but are not limited to,magnetic storage media (e.g., magnetic tapes, floppy disks, hard disks,etc.), optical recording media (e.g., a compact disk read only memory(CD-ROM) and a digital video disk (DVD)), magneto-optical media (e.g., afloptical disk), and hardware devices that are specially configured tostore and execute program instructions, such as a ROM, a random accessmemory (RAM), a flash memory, etc. In some embodiments, data, such asvarious sequences or personal markers described herein, are stored on anon-transitory computer-readable recording medium.

FIG. 10 is a flow diagram of an embodiment of a process for controllinga traffic light based on crowdsourcing data, in accordance with one ormore embodiments.

In some embodiments, the method shown in FIG. 10 is performed by theserver described in FIG. 8, based on the crowdsourcing data. In someembodiments, the traffic light includes a set of electrically operatedsignal lights (e.g., a red light, a yellow light, and a green light)used to direct or control traffic at a road or intersections. In someembodiments, the red light indicates that traffic must stop and thegreen light that it may go, and usually an amber warning light or ayellow light is added between the red and the green.

At operation S1001, by the server, the crowdsourcing data is generatedby using traffic data which is collected by and transmitted from aplurality of vehicles. In some embodiments, the crowdsourcing data isgenerated, in the environment described by FIG. 1, according to themethod described by FIG. 2. In some embodiments, the traffic dataincludes one or more data among traffic volume data, driver age data,and driver reaction speed data. The traffic data is able to betransmitted to the server 800 from one or more vehicles in a compressedstate and/or in an encrypted state.

In some embodiments, the operation S1001 includes: collecting trafficsensing data on a road by using a sensor which senses the plurality ofvehicles and objects surrounding the vehicles; and generating, by theserver, the crowdsourcing data by using the traffic data and thecollected traffic sensing data. In some embodiments, the sensor measuresdistances between the vehicles and distances from the server to thevehicles by illuminating the vehicles with a laser and analyzing areflected light. In some embodiments, the sensor uses Lidar (alsowritten LIDAR, LiDAR or LADAR), which is a remote sensing technologythat measures distance by illuminating a target with a laser andanalyzing the reflected light.

At operation S1002, the traffic light is controlled based on thegenerated data.

In some embodiments, the operation S1002 includes: analyzing a trafficvolume based on the crowdsourcing data; controlling the traffic light toturn on a green light for a longer time than a predetermined time whenthe analyzed traffic volume is higher than a predetermined level;controlling the traffic light to turn on the green light for thepredetermined time when the analyzed traffic volume is equal to thepredetermined level; and controlling the traffic light to turn on thegreen light for a shorter time than the predetermined time when theanalyzed traffic volume is lesser than the predetermined level. In someembodiments, the traffic volume indicates a number of vehicles crossinga section of road per unit time at a selected period and/or an averagespeed of vehicles crossing the section of the road per unit time at theselected period. In some embodiments, the predetermined level is set bya manufacture of the server or by a user of the server, or is updated bythe user of the server. In some embodiments, the predetermined time isset by the manufacture of the server or by the user of the server, or isupdated by the user of the server

In some embodiments, the operation S1002 includes: analyzing a trafficvolume based on the crowdsourcing data; controlling the traffic light toturn on a red light for a shorter time than a predetermined time whenthe analyzed traffic volume is higher than a predetermined level;controlling the traffic light to turn on the red light for thepredetermined time when the analyzed traffic volume is equal to thepredetermined level; and controlling the traffic light to turn on thered light for a longer time than the predetermined time when theanalyzed traffic volume is lesser than the predetermined level. In someembodiments, the operation S1002 includes: controlling a yellow light oran amber warning light of the traffic light based on the analyzedtraffic volume.

In some embodiments, the operation S1002 includes: analyzing an averageage of drivers of the plurality of vehicles based on the crowdsourcingdata; controlling the traffic light to change colors of light (e.g.,from a green light to a yellow light, from the yellow light to a redlight, and from the red light to the green light) in a higher speed thana predetermined speed when the analyzed average age is lesser than apredetermined level; controlling the traffic light to change the colorsof light in the predetermined speed when the analyzed average age isequal to the predetermined level; and controlling the traffic light tochange the colors of light in a lower speed than the predetermined timewhen the analyzed average age is higher than the predetermined level. Insome embodiments, the average age of drivers is inputted by the driverswho are in the vehicles on a road.

In some embodiments, the operation S1002 includes: analyzing a reactionspeed of drivers of the plurality of vehicles based on the crowdsourcingdata; controlling the traffic light to change colors of light in ahigher speed than a predetermined speed when the analyzed reaction speedis higher than a predetermined level; controlling the traffic light tochange the colors of light in the predetermined speed when the analyzedreaction speed is equal to the predetermined level; and controlling thetraffic light to change the colors of light in a lower speed than thepredetermined time when the analyzed reaction speed is slower than thepredetermined level. In some embodiments, the reaction speed is theslowest reaction speed among reaction speeds of the drivers. In someembodiments, the reaction speed is automatically determined based ondriving data of the vehicle on the road. In some embodiments, thedriving data includes one or more of a reaction timing to a red light ofa traffic light, a reaction timing to an interfere of another vehicle,and a reaction trimming to a stop sign on the road.

FIG. 11 is an environment for controlling a traffic light based oncrowdsourcing data, in accordance with one or more embodiments.

In some embodiment, traffic light 1100 is controlled by the methoddescribed in this application is a traffic light 1100 for controllingvehicle traffics on a crossroad. In some embodiments, the server 800 iscombined with the traffic light 1100.

In some embodiments, the traffic light 1100 includes a first trafficlight for controlling vehicle traffics on a first direction of thecrossroad, and a second traffic light for controlling vehicle trafficson a second direction of the crossroad. In these embodiments, at theoperation S1002 of FIG. 10, the first traffic light and the secondtraffic light are differently controlled when the crowdsourcing dataindicates that a first traffic volume on the first direction isdifferent from a second traffic volume on the second direction.

In some embodiments, at the operation S1002 of FIG. 10, the firsttraffic light and the second traffic light are differently controlledwhen the crowdsourcing data indicates that a first average age ofvehicles on the first direction is different from a second average ageof vehicles on the second direction.

In some embodiments, at the operation S1002 of FIG. 10, the firsttraffic light and the second traffic light are differently controlledwhen the crowdsourcing data indicates that a first reaction speed ofdrivers on the first direction is different from a second reaction speedof drivers on the second direction. In some embodiments, the firstreaction speed and the second reaction speed are average reaction speedsof drivers. In some embodiments, the first reaction speed and the secondreaction speed are the slowest reaction speeds of respective drivers.

Each of the above identified instructions and applications cancorrespond to a set of instructions for performing one or more functionsdescribed above. These instructions need not be implemented as separatesoftware programs, procedures, or modules. The memory 750 can includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the computing device 700 can be implemented in hardwareand/or in software, including in one or more signal processing and/orapplication specific integrated circuits.

FIG. 12 is an environment for controlling vehicles by using a trafficcontrol system, in accordance with one or more embodiments.

In an example embodiment, traffic control system 1200 controls a trafficof vehicles 101, 102, and 103. One or more vehicles among the vehicles101, 102, and 103 are configured to perform autonomous driving based onnavigation information received from the traffic control system 1200,and the vehicles 101, 102 and 103 are equipped with a sensor whichcollects driving data, and equipped with a communication unit (notshown) configured to communicate with the traffic control system 1200,via wireless network. In some embodiments, navigation information isreceived by the traffic control system 1200 wirelessly over a networksuch as a Wi-Fi network, a cellular network, an 802.11 network, or aWireless Wide Area Network (WWAN). The vehicles transmit driving data tothe traffic control system 1200, via the network. The driving dataincludes at least one of breaking data, acceleration data and steeringdata. In some embodiments, each of the vehicles 101, 102 and 103communicates with the traffic control system 1200, in accordance with apredetermined cycle of time or, when the vehicles detect an event duringthe driving to the destination. In some embodiments, each of thevehicles 101, 102 and 103 includes a computer system including amicroprocessor configured to provide data processing.

FIG. 13 is a block diagram illustrating a traffic control system forcontrolling vehicles, in accordance with one or more embodiments.

As shown in FIG. 13, traffic control system 1200 includes a Wi-Ficommunication unit 1201, traffic light unit 1203, a control unit 1202,and a Global Positioning System (GPS) unit 1204. In some embodiments,the traffic control system 1200 includes the communication unit 1201 andcontrol unit 1202, without including the traffic light unit 1203 and theGPS unit 1204.

The Wi-Fi communication unit 1201 connects between vehicles and Internetnetwork. In some embodiments, the Wi-Fi communication unit 1201 includesan antenna for data transmission.

In some embodiments, the control unit 1202 to control the vehicles by aWi-Fi network. In order to control the vehicles, the control unit 1202generates crowdsourcing data by using the data collected from thevehicles, and controls the vehicles based on the generated crowdsourcingdata.

The traffic light unit 1203 emits colors or signs of light forcontrolling the vehicles. In some embodiments, the traffic light unit1203 includes one or more light-emitted diodes (LEDs) which emit light.

The GPS unit transmits GPS information to the vehicles, withoutreceiving GPS information from a satellite. In some embodiments, the GPSinformation is fixed and stored in the GPS unit. According to theseembodiments, the vehicles acquire the GPS information from the trafficcontrol system 1200, which is located adjacent to the vehicles, ratherthan from the satellite, which is located far from the vehicle. In theseembodiments, an accuracy of the GPS information is increased, and anaccuracy of vehicle controlling is also increased.

In some embodiments, the GPS unit receives the vehicles' GPS informationwhich includes information on the vehicles' location, from the vehicles,and the control unit 1202 controls the vehicles based on the generatedcrowdsourcing data and the received vehicles' GPS information.

In some embodiments, the GPS unit includes a GPS repeater which receivesa GPS signal from a satellite, and repeats the received GPS signal tothe vehicles. In some implementations, the GPS unit includes anon-ground GPS repeater.

In some embodiments, the control unit 1202 controls the traffic lightunit to control emitting of the colors or signs, based on data collectedfrom the vehicles, via a Wi-Fi network.

In some implementations, vehicles directly controlled by the controlunit are autonomous vehicles, and vehicles controlled via the trafficlight unit are non-autonomous vehicles. The autonomous vehicles includesvehicles which are partially autonomous-controlled by a control unit andpartially manual-controlled by a driver. The vehicles directlycontrolled by the control unit are autonomous vehicles may be defined asconnected cars, and vehicles controlled via the traffic light unit maybe defined as non-connected cars.

In some implementations, the control unit 1202 controls the trafficlight unit 1203 to stop a traffic of non-autonomous vehicles when thecontrol unit is controlling a traffic of autonomous vehicles.

In some implementations, the control unit 1202 controls the trafficlight unit 1203 to emit a waiting signal to autonomous vehicles, whenthe traffic light unit is controlling a traffic of non-autonomousvehicles.

In some implementations, the control unit 1202 controls the trafficlight unit 1203 by sending a fragmented signal to the traffic lightunit. An example of the fragmented signal is shown in FIG. 15. In someimplementations, the fragmented signal is included in a Wi-Fi signaltransmitted from the control unit to the traffic light unit. In someimplementations, the fragmented signal is fragmented for controllingsigns or colors emitted from the traffic light unit.

In some implementations, the control unit 1202 generates crowdsourcingdata by using the data collected from the vehicles, and controls thetraffic light unit based on the generated crowdsourcing data.

In some implementations, the traffic control system 1200 includes one ormore elements shown in FIGS. 7-9 for performing its functions.

In some embodiments, one or more of the elements 1201, 1202, 1203 and1204 is/are implemented by, or include(s), one or more processors and/orapplication-specific integrated circuits (ASICs) specified forrespectively corresponding operations and functions described herein inthe present disclosure. In some embodiments, the methods according to atleast one embodiment of the present disclosure are implemented ascomputer-readable code on a non-transitory computer-readable recordingmedium. The non-transitory computer-readable recording medium includesany data storage device configured to store data readable and/orexecutable by a computer system. Examples of the non-transitorycomputer-readable recording medium include, but are not limited to,magnetic storage media (e.g., magnetic tapes, floppy disks, hard disks,etc.), optical recording media (e.g., a compact disk read only memory(CD-ROM) and a digital video disk (DVD)), magneto-optical media (e.g., afloptical disk), and hardware devices that are specially configured tostore and execute program instructions, such as a ROM, a random accessmemory (RAM), a flash memory, etc. In some embodiments, data, such asvarious sequences or personal markers described herein, are stored on anon-transitory computer-readable recording medium.

FIG. 14 is a flow diagram of a method for controlling a traffic, inaccordance with one or more embodiments.

In some embodiments, the method shown in FIG. 14 is performed by thetraffic control system described in FIG. 13. In some embodiments, atraffic light includes a set of electrically operated signal lights(e.g., a red light, a yellow light, and a green light) used to direct orcontrol traffic at a road or intersections. In some embodiments, the redlight indicates that traffic must stop and the green light that it maygo, and usually an amber warning light or a yellow light is addedbetween the red and the green.

At operation S1401, connecting, by a Wi-Fi network, between vehicles andInternet network.

At operation S1402, collecting, by the Wi-Fi communication unit, datafrom the vehicles.

At operation S1403, generating, by the control unit, crowdsourcing databy using the data collected from the vehicles.

At operation S1404, controlling, by the control unit, the vehicles basedon the generated crowdsourcing data. In some embodiments, the collecteddata include traffic data as set forth above.

In some embodiments, the method further includes steps of: transmitting,by the GPS unit, GPS information to the vehicles, without receiving GPSinformation from a satellite. The GPS information is fixed and stored inthe GPS unit.

In some embodiments, the method further includes steps of: receiving, bythe GPS unit, the vehicles' GPS information which includes informationon the vehicles' location, from the vehicles; and controlling, by thecontrol unit, the vehicles based on the generated crowdsourcing data andthe received vehicles' GPS information.

In some embodiments, the method further includes steps of: receiving, bythe GPS repeater included in the traffic control system, a GlobalPositioning System (GPS) signal from a satellite; and repeating, by theGPS repeater, the received GPS signal to the vehicles.

FIG. 15 is a flow diagram of a method for controlling a traffic, inaccordance with one or more embodiments.

The operations S1401-S1404 are the same as described with respect toFIG. 14.

In some embodiments, the method further includes operations S1403 andS1404. At the operation S1403, emitting, by the traffic light unit,colors or signs of light for controlling the vehicles. At the operationS1404, controlling, by the control unit, the traffic light unit tocontrol emitting of the colors or signs, based on the collected data. Insome embodiments, the collected data include traffic data as set forthabove.

In some embodiments, vehicles directly controlled by the control unitare autonomous vehicles, and vehicles controlled via the traffic lightunit are non-autonomous vehicles.

In some embodiments, the method further includes a step of controllingthe traffic light unit to stop a traffic of non-autonomous vehicles whenthe traffic control system is controlling a traffic of autonomousvehicles.

In some embodiments, the method further includes a step of controllingthe traffic light unit to emit a waiting signal to autonomous vehicles,when the traffic control system is controlling a traffic ofnon-autonomous vehicles.

In some embodiments, the method further includes a step of controllingthe traffic light unit by sending a fragmented signal to the trafficlight unit.

In some embodiments, the method further includes steps of generatingcrowdsourcing data by using the data collected from the vehicles; andcontrolling the traffic light unit based on the generated crowdsourcingdata.

FIG. 16 is a schematic illustrating a conceptual partial view of afragmented signal for controlling signs or colors emitted from a trafficlight unit, in accordance with one or more embodiments.

FIG. 16(a) is a conceptual partial view of a non-fragmented (general)signal, and FIG. 16(b) is a conceptual partial view of a fragmentedsignal. In some embodiments, a traffic light includes a set ofelectrically operated signal lights (e.g., a first color light 1501, anda second color light 1502) used to direct or control traffic at a roador intersections. In some embodiments, the first light 1501 indicatesthat traffic may go and the second light 1502 that it must stop. FIG.16(a) is a conceptual partial view of changing colors of four trafficlights for each road in a cross-section. FIG. 16(b) is a conceptualpartial view of data structures for traffic regulation data and specifictraffic regulation data for each vehicle.

In some embodiments, the control unit of FIG. 13 is able to control thetraffic light unit of FIG. 13 by sending the fragmented signal to thetraffic light unit. In these embodiments, the traffic control system isable to tightly control vehicles in accordance with a traffic condition.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present disclosure(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required; avariety of optional components are described to illustrate the widevariety of possible embodiments of the present disclosure.

Further, although process operations, method operations, algorithms orthe like may be described in a sequential order, such processes, methodsand algorithms may be configured to work in alternate orders. Anysequence or order of operations that may be described does notnecessarily indicate a requirement that the operations be performed inthat order. The operations of processes described herein may beperformed in any order practical. Further, some operations may beperformed simultaneously.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the presentdisclosure need not include the device itself.

The illustrated operations of FIGS. 2-4 show certain events occurring ina certain order. In alternative embodiments, certain operations may beperformed in a different order, modified or removed. Moreover,operations may be added to the above described logic and still conformto the described embodiments. Further, operations described herein mayoccur sequentially or certain operations may be processed in parallel.Yet further, operations may be performed by a single processor or bydistributed processors.

The foregoing description of various embodiments of the disclosure hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the disclosure to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the disclosurebe limited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe disclosure. Since many embodiments of the disclosure can be madewithout departing from the spirit and scope of the disclosure, thedisclosure resides in the claims hereinafter appended.

The invention claimed is:
 1. A system comprising: a server including: acommunication unit configured to connect between vehicles and aninternet network; and a control unit configured to control the vehiclesby a wireless network, generate crowdsourcing data by using the datacollected from sensors equipped in the vehicles, transmit the generatedcrowdsourcing data to electronic control units (ECUs) embedded in thevehicles that update navigation information stored in storages in theECUs embedded in the vehicles based on the crowdsourcing datatransmitted from the control unit to the ECUs embedded in the vehicles,wherein the generated crowdsourcing data is combined, by the ECUsembedded in the vehicles, with the data collected from the sensorsequipped in the vehicles, to update navigation information stored in thestorages in the ECUs embedded in the vehicles, update navigationinformation stored in the control unit based on the generatedcrowdsourcing data, and transmit the updated navigation information bythe control unit, to the ECUs embedded in the vehicles.
 2. The systemaccording to claim 1, further comprising a traffic light unit configuredto emit colors or lighted signs for controlling the vehicles, whereinthe control unit is further configured to control the traffic light unitto control emitting of the colors or signs, based on data collected fromthe vehicles, via the wireless network.
 3. The system according to claim2, wherein the vehicles comprises autonomous vehicles and non-autonomousvehicles, wherein the autonomous vehicles are directly controlled by thecontrol unit, and wherein the non-autonomous vehicles are controlled viathe traffic light unit.
 4. The system according to claim 1, wherein thevehicles comprise: a communication unit configured to transmit a requestfor a route to a destination to a server, and receive navigationinformation retrieved according to the request from the server; a sensorconfigured to generate sending data; and a control unit configured tocontrol an operation of the vehicle based on the received navigationinformation, collect, by the sensor, the sensed data during drivingaccording to the received navigation information, generate driving databased on the collected sensor data, transmit the generated driving datato the server, receive crowdsourcing data from the server, update thereceived navigation information based on the received crowdsourcingdata, and control the operation of the vehicle based on the updatednavigation information.
 5. The system according to claim 1, furthercomprises: a positioning system unit configured to transmit locationinformation of the traffic control system, fixed and stored in thepositioning system unit, to the vehicles, without receiving positioningsystem information from a satellite.
 6. The system according to claim 1,further comprises: a positioning system unit configured to receive thevehicles' positioning system information which includes information onthe vehicles' location, from the vehicles.
 7. The system according toclaim 1, further comprises: a positioning system repeater configured toreceive a positioning system signal from a satellite, and repeat thereceived positioning system signal to the vehicles.
 8. The systemaccording to claim 1, wherein the communication unit comprises awireless router.
 9. The system according to claim 1, wherein the controlunit is configured to control the traffic light unit which provides anindication to an operator of a non-autonomous vehicle such that theoperator knows the corresponding vehicle must be stopped when thecontrol unit is responsible for controlling the passage of a desiredgroup of vehicles.
 10. The system according to claim 1, wherein thecontrol unit is configured to control the traffic light unit to emit awaiting signal to autonomous vehicles to influence its behavior, whenthe traffic light unit is responsible for controlling the passage of adesired group of vehicles.
 11. The system according to claim 1, whereinthe control unit is configured to control the traffic light unit bysending a fragmented signal to the traffic light unit.
 12. The systemaccording to claim 11, wherein the fragmented signal is included in awireless signal transmitted from the control unit to the traffic lightunit.
 13. The system according to claim 11, wherein the fragmentedsignal is fragmented for controlling signs or colors emitted from thetraffic light unit.
 14. A method for controlling a traffic, performed bya traffic control system including a server including a communicationunit, and a control unit, the method comprising: connecting, by thecommunication unit, via a wireless network, between vehicles and aninternet network; collecting, by the communication unit, data fromsensors equipped in the vehicles; generating, by the control unit,crowdsourcing data by using the data collected from the sensors equippedin the vehicles; transmitting, by the communication unit, the generatedcrowdsourcing data to electronic control units (ECUs) embedded in thevehicles that update navigation information stored in storages in theECUs embedded in the vehicles based on the crowdsourcing datatransmitted from the control unit to the ECUs embedded in the vehicles,wherein the generated crowdsourcing data is combined, by the ECUsembedded in the vehicles, with the data collected from the sensorsequipped in the vehicles, to update navigation information stored in thestorages in the ECUs embedded in the vehicles; updating, by the controlunit, navigation information stored in the control unit based on thegenerated crowdsourcing data; and transmitting, by the communicationunit, the updated navigation information by the control unit, to theECUs embedded in the vehicles.
 15. The method according to claim 14,wherein the traffic control unit further comprises a traffic light unit,and the method further comprises: emitting, by the traffic light unit,colors or lighted signs for controlling the vehicles; and controlling,by the control unit, the traffic light unit to control emitting of thecolors or signs, based on the crowdsourcing data.
 16. The methodaccording to claim 15, wherein the vehicles comprises autonomousvehicles and non-autonomous vehicles, wherein the autonomous vehiclesare directly controlled by the control unit, and wherein thenon-autonomous vehicles are controlled via the traffic light unit. 17.The method according to claim 14, wherein the traffic control unitfurther comprises a positioning system unit, and the method furthercomprises: transmitting, by the positioning system unit, locationinformation of the traffic control system, fixed and stored in thepositioning system unit, to the vehicles, without receiving positioningsystem information from a satellite.
 18. The method according to claim14, wherein the traffic control unit further comprises a positioningsystem unit, and the method further comprises: receiving, by thepositioning system unit, the vehicles' positioning system informationwhich includes information on the vehicles' location, from the vehicles.19. The method according to claim 14, wherein the traffic control unitfurther comprises a positioning system repeater, and the method furthercomprises: receiving, by the positioning system repeater, a positioningsystem signal from a satellite; and repeating, by the positioning systemrepeater, the received positioning system signal to the vehicles.
 20. Asystem comprising: a server comprising: a communication unit configuredto connect between vehicles and Internet network; a control unitconfigured to control the vehicles by a wireless network, generatecrowdsourcing data by using the data collected from sensors equipped inthe vehicles, transmits generated crowdsourcing data to electroniccontrol units (ECUs) embedded in the vehicles that update navigationinformation stored in storages in the ECUs embedded in the vehiclesbased on the crowdsourcing data transmitted from the control unit to theECUs embedded in the vehicles, wherein the generated crowdsourcing datais combined, by the ECUs embedded in the vehicles, with the datacollected from the sensors equipped in the vehicles, to updatenavigation information stored in the storages in the ECUs embedded inthe vehicles, update navigation information stored in the control unitbased on the generated crowdsourcing data, and transmits the updatednavigation information by the control unit, to the ECUs embedded in thevehicles; the vehicles comprising: a communication unit configured totransmit a request for a route to a destination to a server, and receivenavigation information retrieved according to the request from theserver; a sensor configured to generate sending data; and a control unitconfigured to control an operation of the vehicle based on the receivednavigation information, collect, by the sensor, the sensed data duringdriving according to the received navigation information, generatedriving data based on the collected sensor data, transmit the generateddriving data to the server, receive crowdsourcing data from the server,update the received navigation information based on the receivedcrowdsourcing data, and control the operation of the vehicle based onthe updated navigation information.